Unified Multi-Task Convolutional Neural Networks for Enhanced Disease Detection and Localization in Medical Imaging

Deep learning has been integrated with medical imaging, which is highly beneficial information processing no longer possible to be handled by human observers. Abstract This work introduces a multitask convolutional neural network (CNN) architecture for the joint localization and identification of diseases in medical images. The method consists of a massive data collection and preprocessing step, which is then followed by designing CNN architecture that incorporates shared layers for feature extraction as well as task-specific branches with attention mechanisms. It is trained and tested on different types of medical data - so you can feel confident it will be reliable in your clinical setting. The multitask CNN obtained an accuracy of 91.5% for disease diagnosis, with a precision, recall, and F1 score (avg) = [0.90200098] [0.88799303] [0.89397912]. It does the job for localization at 80.3% of Intersection over the Union (IoU) metric score. This result outperforms the following baselines: NDDR-CNN (89.7% accuracy, 78.2% IoU), and single-task CNN (87.8 %accuracy, 75.6% IOU). Independent test sets were also used to examine the model and a user-friendly clinical interface was created for practical feasibility in medical settings. Our findings demonstrate that multitask learning models have the potential to substantially improve medical image analysis by providing better diagnostic strategies, which enable us to correctly and comprehensively diagnose diseases and, as a result, find them accurately in terms of detection sensitivity or localization specificity for disease classification.